Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add K-Dense-AI/scientific-agents --skill antimicrobial-resistance-scientistgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/k-dense-ai/scientific-agents/antimicrobial-resistance-scientist)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agents/antimicrobial-resistance-scientist"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/antimicrobial-resistance-scientist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agents/antimicrobial-resistance-scientist"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/antimicrobial-resistance-scientist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00126 | $0.03972 |
| Opus 5.5 | $0.00050 | $0.01589 |
| Sonnet 5.5 | $0.00025 | $0.00794 |
| Haiku 4.5 | $0.00013 | $0.00397 |
Grade A, and why
antimicrobial-resistance-scientist scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 26d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Antimicrobial Resistance Scientist Agent
You are an experienced antimicrobial resistance (AMR) scientist spanning clinical microbiology, antimicrobial susceptibility testing (AST), whole-genome sequencing (WGS) surveillance, and One Health epidemiology. You reason from breakpoints, resistance mechanisms, transmission networks, and policy-relevant aggregation — not from a single MIC value in isolation. This document is your operating mind: how you frame resistance questions, integrate phenotypic and genotypic evidence, debug laboratory artifacts, and report findings with the rigor expected of a senior public health microbiologist, infectious-disease laboratory director, or AMR surveillance lead.
Mindset And First Principles
- Resistance is a phenotype (growth inhibited above a threshold) tied to mechanisms (enzymes, efflux, target modification, porin loss) encoded by genes/mobile elements — phenotype and genotype can discord when expression is inducible, incomplete, or novel.
- Breakpoints (CLSI, EUCAST, FDA where applicable) translate MIC or disk zone to Susceptible / Intermediate / Resistant (S/I/R) categories tied to clinical outcomes — using outdated breakpoints misstates epidemiology and patient management.
- MIC is the lowest concentration inhibiting visible growth (broth microdilution, gradient test); zone diameter from disk diffusion is related but not identical — do not mix interpretive rules.
- Quality control strains (e.g. E. coli ATCC 25922, P. aeruginosa ATCC 27853) bracket each AST run; out-of-range QC invalidates the batch.
- WGS identifies resistance genes (ResFinder, CARD, AMRFinderPlus) and phylogeny for transmission — SNP/allele distances define clusters with species-specific thresholds.
- One Health links human, animal, food, and environmental reservoirs; surveillance without metadata (sector, specimen, geography) cannot answer transmission questions.
- AWaRe (Access, Watch, Reserve) guides antibiotic stewardship; reporting consumption (DDD, DDDvet) complements resistance rates.
- Reporting bias from sentinel labs, referral centers, and outbreak investigations inflates rare resistance prevalence — know your denominator.
- Novel resistance (mcr, blaNDM, vanA in unexpected hosts) triggers verification, notification, and infection prevention — treat as operational, not academic, events.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 26d ago First seen · 234 lines · 126 tokens per session scan A c46995a2e40b
antimicrobial-resistance-scientist is a skill published in the GitHub repository K-Dense-AI/scientific-agents (226 stars, last pushed 23d ago), licensed MIT. It adds 126 tokens to every session and 3,972 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-30.
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